Knowledge Graphs (KGs) about real-world entities and their properties are an important asset in many AI applications, but web-scale KGs generally store only positive statements. Due to this incompleteness, absent statements are considered unknown rather than false. This book, Enriching Open-world Knowledge Graphs with Expressive Negative Statements, which won the SWSA Distinguished Dissertation Award for its author Hiba Arnaout in 2024, makes the case for enriching KGs with informative negative statements to enhance their usability in applications such as question answering and entity summarization. The vast number of potential candidate statements raises four main challenges: correctness or plausibility; salience; coverage of subjects; and complexity, so the book makes a case for selection from encyclopedic, well-canonicalized, and commonsense, less-canonicalized, open-world KGs, and defines three types of negative statement: grounded, universally absent, and conditional. It presents the peer-based negation-inference method for compiling lists of salient negatives about entities from encyclopedic KGs, and the pattern-based query log extraction method is proposed for extracting salient negatives from rich textual sources. The 'UnCommonsense' method, whereby users can closely inspect what the method produces at every step, as well as browse negatives about 8K everyday concepts, is suggested as a means to generate salient negative phrases about everyday concepts in less-canonicalized commonsense KGs. Finally, candidate statements are ranked using semantic-similarity aware frequency measures, and two prototype systems are described to facilitate exploring these methods and their results. The peer-based negation inference method is also used to create the first large-scale dataset on demographics and outliers in communities of interest, and its usefulness is shown with regard to cases such as identifying under-represented groups. Exploring innovative methods for enhancing knowledge graphs with expressive negative statements, this book will be a valuable resource for those working in knowledge representation, AI, and NLP.
